Learn Data Science – Do Programming using Python & R

Course Name: – Learn Data Science – Do Programming using Python & R

Date & Time: – Sat 07th Dec to Sat 25th Jan 2020 every Saturday from 10:00 AM to 1:00 PM

Cost: – 

Booking between 03 Nov to 23 Nov 2019 – 1000 INR discount, you pay 19000 INR
Booking between 24 Nov to 30 Nov 2019 – 500 INR discount, you pay 19500 INR
Booking between 01 Dec to 06 Dec 2019 – 0 INR discount, you pay 20000 INR

How to Join?

Click Here


Good news for enrolled candidates of Data Science training where they will get chance to attend FREE sessions on Mathematics which are pre-requisite required to accomplish Data Science training, see syllabus and other details here

Key Features

  • No PPT’s completely Hands-on Data Science – R programming training.
  • Installation required in your laptop for training
    • R download link, get from here
    • Python download link, get from here
    • IPython download link, get from here
    • For MAC system download link of Python, NumPy, SciPy & matplotlib get from here
  • All at only 14000 INR

What is Data Science? 

Why to choose Data Science as career? 

Executional Syllabus: – 

Python Quick Start and Basics Installing Python framework and Pycharm IDE.
Creating and Running your first Python project.
Python is case-sensitive
Variables, data types, inferrence & type()
Python is a dynamic language
Comments in Python
Creating function, whitespaces & indentation
Importance of new line
List in python, Index, Range & Negative Indexing
For loops and IF conditions
PEP, PEP 8, Python enhancement proposal
Array vs Python
Reading text files in Python
Casting and Loss of Data
Referencing external libararies
Applying linear regression using sklearn
Creating classes and objects.
Zip and UnZip
Numpy and Pandas
Array vs Pandas Referencing Numpy and Pandas
Creating a Numpy array
Numpy Array vs Normal Python array
Why do we need Pandas?
Revising Arrays vs Numpy Array vs Pandas
Machine Learning Fundamentals – BOW, Vectors and Labels
What is Machine learning?
Algorithm and Training data.
Models in Machine Learning.
Features and Labels.
Bag of words.
Implementing BOW using SKLearn.
The fit Method.
The transform Method.
Understanding TD and IDF
Corupus / Documents, Document and Terms.
Understanding TF
Understanding IDF
Performing calculations of TF IDF.
Implementing TF IDF using SkLearn
IDF calculation in SkLearn.
Regression Linear Regression Models
Non Linear Regression Models
Classification Decision Tree
Logistic Regression
Support Vector Machinesa
K-means Clustering and Case Study
DBSCAN Clustering and Case study
Hierarchical Clustering
Apriori Algorithm
Candidate Generation
Visualization on Associated Rules